Neuromancer

New feature: Script to generate documents for offline configuration of LLM/RAG-based systems
New feature: In-depth notebook comparing and contrasting RL vs DPC for building controls
New feature: Library now supports Python 3.11
New feature: Updated Node class than can accept instantiated Variables in its constructor

This research was partially supported by the Energy Efficiency and Renewable Energy, Building Technologies Office under the “Dynamic decarbonization through autonomous physics-centric deep learning and optimization of building operations” and the “Advancing Market-Ready Building Energy Management by Cost-Effective Differentiable Predictive Control” projects. This project was also supported from the U.S. Department of Energy, Advanced Scientific Computing Research program, under the Uncertainty Quantification for Multifidelity Operator Learning (MOLUcQ) project (Project No. 81739).
PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL0-1830.

Part of this work was supported by the research group of [Ján Drgoňa](https://drgona.github.io/) in the [Department of Civil and Systems Engineering](https://engineering.jhu.edu/case/) and the [Ralph S. O’Connor Sustainable Energy Institute (ROSEI)](https://energyinstitute.jhu.edu/) at Johns Hopkins University (JHU).

- New feature: Multi-fidelity Kolgomorov Arnold Networks for SOTA function approximation
- New feature: Load forecasting on building energy systems tutorials
- New feature: Transformer block

This research was partially supported by the Energy Efficiency and Renewable Energy, Building Technologies Office under the “Dynamic decarbonization through autonomous physics-centric deep learning and optimization of building operations” and the “Advancing Market-Ready Building Energy Management by Cost-Effective Differentiable Predictive Control” projects. This project was also supported from the U.S. Department of Energy, Advanced Scientific Computing Research program, under the Uncertainty Quantification for Multifidelity Operator Learning (MOLUcQ) project (Project No. 81739).
PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL0-1830.

+ Enhancement: Now supports integration of all Lightning hooks into the Neuromancer Lightning trainer. Please refer to Lightning examples README for more information
+ Deprecated WandB hyperparameter tuning via `LitTrainer` for now
+ New feature: TorchSDE integration with Neuromancer core library, namely `torchsde.sdeint()`. Motivating example for system ID on stochastic process found in examples/sdes/sde_walkthrough.ipynb
+ New feature: Stacked physics-informed neural networks
+ New feature: SINDy -- sparse system identification of nonlinear dynamical systems

New Feature: PyTorch Lightning Integration with NeuroMANCER core library. All these features are opt-in.
- Code simplifications: zero boilerplate code, increased modularity
- Added ability for user to define custom training logic
- Easy support for GPU and multi-GPU training
- Easy Weights and Biases (https://wandb.ai/site) hyperparameter tuning and Tensorboard Logging

+ New feature: Update violation energy for projected gradient 110 (based on idea 86).
+ Reverted `psl.nonautonomous.TwoTank` `(umin, umax)` bounds to `(0.5, 0.5)` for numerical stability 105
+ Added new unit tests for `problem.py` and `system.py` 107
+ Automated docs build from `master -> gh-pages` 107
+ Fixed positional arg error and added support for Time data in `file_emulator.py` 119
+ Fixed a bug in `System` which caused incorrect visualization of the computational graph
+ Minor updates to examples